LIMBIC CORTICAL BASES OF ASSOCIATIVE LEARNING

这是一份申请NIMH研究科学家奖的申请书 脑机制的实验/理论结合研究 潜在的学习和记忆。具体目标是开发一种 基于生物的数学模型和计算机模拟 海马体的功能特性，这是大脑中的一个区域 对学习和记忆是必不可少的。将使用三种方法来 实现这一目标：i)将采用非线性系统分析方法 用于建立海马体的输入/输出模型，ii)场 将用理论的方法来制定详细的生理学和 海马体的解剖模型，以及III)用于 光计算和VLSI设计将用于多个站点 海马体模型的记录和硬件表示。这个 生成的模型将用于解释与学习相关的活动 经典行为动物的海马神经元记录 条件反射。在这些分析的基础上，希望有一个更好的 关于两国之间的关系，将会出现理解 网络动力学、突触可塑性和信息存储 大脑。

This is an application for an NIMH Research Scientist Award to implement a combined experimental/theoretical study of the brain mechanisms underlying learning and memory. The specific goal is to develop a biologically based mathematical model and computer simulation of the functional properties of the hippocampus, a region of the brain which is essential for learning and memory. Three approaches will be used to reach this objective: i) a nonlinear systems analytic approach will be used to develop an input/output model of the hippocampus, ii) a field theory approach will be used to develop a detailed physiological and anatomical model of the hippocampus, and iii) technologies used in optical computing and VLSI design will be used for multiple site recording and hardware representations of models of the hippocampus. The resulting models will be used to interpret learning-related activity of hippocampal neurons recording from behaving animals during classical conditioning. On the basis of these analyses, it is hoped that a better understanding will emerge with respect to the relationships between network dynamics, synaptic plasticity, and information storage in the brain.